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Air Quality Impact of Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa) Eloise A. Marais*,† and Christine Wiedinmyer‡ †

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States National Center for Atmospheric Research, Boulder, Colorado 80301, United States

‡

S Supporting Information *

ABSTRACT: Anthropogenic pollution in Africa is dominated by diffuse and inefficient combustion sources, as electricity access is low and motorcycles and outdated cars proliferate. These sources are missing, out-of-date, or misrepresented in state-of-the-science emission inventories. We address these deficiencies with a detailed inventory of Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa) for 2006 and 2013. Fuelwood for energy is the largest emission source in DICE-Africa, but grows from 2006 to 2013 at a slower rate than charcoal production and use, and gasoline and diesel for motorcycles, cars, and generators. Only kerosene use and gas flaring decline. Increase in emissions from 2006 to 2013 in this work is consistent with trends in satellite observations of formaldehyde and NO2, but much slower than the explosive growth projected with a fuel consumption model. Seasonal biomass burning is considered a large pollution source in Africa, but we estimate comparable emissions of black carbon and higher emissions of nonmethane volatile organic compounds from DICE-Africa. Nitrogen oxide (NOx ≡ NO + NO2) emissions are much lower than from biomass burning. We use GEOS-Chem to estimate that the largest contribution of DICE-Africa to annual mean surface fine particulate matter (PM2.5) is >5 μg m−3 in populous Nigeria.

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INTRODUCTION Air pollution in Africa is dominated by seasonal open field burning (biomass burning);1 however, anthropogenic activity may surpass biomass burning as the largest pollution source by 2030.2 Africa is experiencing rapid economic and population growth that will inevitably degrade air quality. Sustained economic growth requires access to reliable and affordable electricity. Currently the majority of Africans depend on inefficient sources of combustion for their energy and transport needs.3 Inefficient combustion results in direct emission of fine particles (PM2.5, aerosols with diameter less than 2.5 μm) like black carbon (BC) and organic carbon (OC), and formation of secondary organic aerosol (SOA) and ozone from photooxidation of nonmethane volatile organic compounds (NMVOCs). PM2.5 and ozone are hazardous to human health. Here we develop a detailed inventory of anthropogenic Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa) to determine the impact of these sources on ambient air quality. The population in Africa has doubled since the mid 1980s,4 but average electricity access has only increased by 12%.5 Currently access to reliable electricity is less than 50% in most African countries. In some countries (Chad, Burundi, Malawi, and Liberia) access is below 10%.5 Most people rely on crop residue, fuelwood, charcoal, kerosene, and backup generators for energy. The mix of fuel varies for urban and rural populations, determined largely by access to fuel and affluence. © 2016 American Chemical Society

Rural households typically use fuelwood and crop residue, whereas urban households and businesses use a mix of fuelwood, charcoal, and backup generators.6,7 Kerosene is used in urban and rural homes for cooking and lighting.8,9 Rapid urbanization and economic growth are contributing to expansion in backup generator use and charcoal production.10,11 Charcoal is formed by slow-burning wood under anoxic conditions and occurs predominantly in rural areas close to forests.12 Proliferation of motorcycles, a vehicle fleet dominated by outdated cars, poor road management, and low-quality fuel exacerbate pollution in dense urban centers.13,14 The problem will worsen with increased demand for cars and motorcycles.15 Regular fuel shortages in Nigeria, where one in five Africans reside, has energized an informal industry of ad hoc oil refining in the Niger Delta. As much as 37 500 barrels of crude oil is refined daily,16 nearly 2% of crude oil production in Nigeria.17 A standard practice at conventional oil fields in Africa, in particular Nigeria, is to flare natural gas, as oil fields were established when natural gas was not marketable.17 Flaring is a major source of pollution, since flares in Africa operate at low Received: Revised: Accepted: Published: 10739

May 24, 2016 September 5, 2016 September 9, 2016 September 9, 2016 DOI: 10.1021/acs.est.6b02602 Environ. Sci. Technol. 2016, 50, 10739−10745

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Environmental Science & Technology combustion efficiency (90%) from cars. In general, cars are a larger source of trace gases than motorcycles, whereas cars and motorcycles make near-equivalent contributions to BC and OC.

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RESULTS AND DISCUSSION Figure 1 shows the distribution of NMVOCs (14 Tg) and BC (0.67 Tg C) emissions from DICE-Africa for 2006, which follow closely the spatial distribution of population (Figure S1). Emissions are high in dense rural and urban areas along the Nile River, throughout northern Ethiopia, in northern Nigeria, the Niger Delta, and surrounding Lake Victoria in Kenya, Uganda, Rwanda, and Burundi. BC emissions in the Sahara Desert and the Gulf of Guinea are from gas flaring. Liousse et al.2 forecast that anthropogenic emissions will surpass biomass burning pollution by 2030, but total NMVOC emissions in Figure 1 already exceed estimates from biomass burning for the same year: 5.9 Tg and 6.2 Tg from the FINN46 and GFED343 inventories, respectively. Our estimate of BC emissions is similar to the range from biomass burning: 1.1 (FINN) and 0.67 (GFED3) Tg C. Figure 2 shows the contribution of sectors to emissions of CO, BC, and NOx in 2006. Sector emissions of other species 10741

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Figure 3. Comparison of emission inventory estimates and trends for Africa. Results from this study (solid black line) are compared to emission totals and trends from the regional Liousse et al. inventory, and global inventories: EDGAR v4.2, RETRO v2.0, ACCMIP, Bond et al. inventories, and the RCP 4.5 and 8.5 scenarios. Liousse et al. values for 2030 are their business-as-usual “REF” scenario. Gray lines show emissions from this study with hard coal emissions of NOx and SO2 from EDGAR v4.2 (dashed gray lines) and trash burning emissions of CO, NMVOCs, OC, and BC from Wiedinmyer et al. (solid gray lines).

estimate higher emissions of the other aromatics toluene and xylenes (not shown). Aromatics (benzene, toluene, and xylenes) are SOA precursors, so their contribution will substantially alter secondary sources of PM2.5.50 Ozone production is also sensitive to the mix of NMVOC species.51,52 All emissions, except BC, increase from 2006 to 2013 in this work. Decline in BC of 0.2% a−1, not seen in the other emission inventories, is due in large part to reduced kerosene use. Kerosene is prohibitively expensive, and cheaper alternatives, like solar power, are becoming more readily available.9 Flaring of natural gas is a smaller source of BC than kerosene (Figure 2), but is declining rapidly (5.0% a−1). The reason for the decline in flaring is speculative, but proposed causes include increased utilization of natural gas for energy generation, reinjection of natural gas into oil wells to increase crude oil production, and disposal of natural gas by venting instead of flaring.19 The rate of change in emissions in this study is very different to Liousse et al. projected emissions for 2030. Liousse et al. forecast 2030 emissions with a fuel consumption model and assume no environmental regulation. Liousse et al. project steep increases in NMVOCs and NOx emissions from 2005 to 2030 of 9.6% a−1 and 19% a−1, respectively. By comparison, increase in pollution from 2006 to 2013 in this work is 2.2% a−1 for NMVOCs and 2.5% a−1 for NOx. Positive trends in satellite observations of total column formaldehyde (a high-yield oxidation product of NMVOCs) are only detected over three cities in Africa and range from 1.1 to 2.7% a−1.53 These compare more favorably with NMVOC emission trends in this work (2.2% a−1) than in Liousse et al. (9.6% a−1). Increase in tropospheric columns of NO2 observed from space range from

Figure 3 compares emission totals and trends from this study with values from global and regional inventories. BC emissions in this work for 2006 are consistent with values from the Liousse et al.2 regional inventory (henceforth referred to as “Liousse et al.”) for 2005, but we obtain lower NOx and higher NMVOC and CO emissions than the Liousse et al. inventory. They estimate NOx, NMVOC, and CO emission factors for semideveloped and developing nations by scaling developed country NOx, CO, and NMVOC emission factors each by the same amount, so that NMVOC-to-NOx and CO-to-NOx emission ratios are the same for different development categories. Reliance on inefficient combustion sources for energy in semideveloped and developing nations should lead to an increase in NMVOC-to-NOx and CO-to-NOx emission ratios. SO2 emissions in this work are more than an order of magnitude lower than Liousse et al. SO2 emission factors for diesel and gasoline for cars and motorcycles in this work (Table S6) are much lower than emission factors in Liousse et al., as we assume that the majority of diesel and gasoline is refined from low-sulfur crude oil (2.5 wt % sulfur) that is typical of African oil fields.47 Liousse et al. include pollution from hard coal in their inventory, but adding EDGAR v4.2 hard coal SO2 emissions to SO2 emissions estimated in this work does not resolve the discrepancy (Figure 3; gray dashed line). Total NMVOC emissions from the global inventories EDGAR v4.2 in 2006, ACCMIP in 2000, 24 and the Representative Concentration Pathways (RCP) 4.548 and 8.549 in 2005 are similar to this study, but with very different species contributions. Formaldehyde and benzene, for example, are higher in this work than the global inventories by at least a factor of 2.3 for formaldehyde and 1.4 for benzene. We also 10742

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Environmental Science & Technology 1.5 to 13% a−1 and are detected in most large African cities.54,55 This can be compared to 2.5% a−1 in this work and 19% a−1 in Liousse et al. Emissions from the RCP scenarios in Figure 3 are determined assuming strict environmental policy, and so both RCP scenarios predict similar 2030 emissions of NOx, NMVOCs, and OC. Consistent DICE-Africa and RCP trends in CO, NOx, NMVOC, and OC emissions may imply delay in the rapid increase in pollution projected by Liousse et al., as strict environmental policies imposed by the RCP scenarios seem unrealistic for Africa. The continent is yet to experience rapid industrialization and air quality degradation that precede implementation of air quality policy. We use GEOS-Chem to determine the impact of diffuse and inefficient combustion emissions on air quality in 2006. Figure 4 shows maps of the contribution of diffuse and inefficient

50°S (Figure S4), offsetting the Earth’s radiative balance in a region that is very sensitive to changes in ozone.57 There are large data gaps in Africa that contribute to uncertainties in the inventory developed in this work. Countrylevel fuel use data are often based on limited field measurements that are then extrapolated to the rest of the country. Data are lacking on the location of paved and unpaved roads to map car and motorcycle use and estimate fugitive road dust emissions. There is also insufficient data to distinguish diesel/ gasoline use by passenger and nonpassenger vehicles. Information on the extent and magnitude of electronic waste burning and the associated emission factors is also limited. Emission factors are missing for important pollution sources in Africa, in particular NMVOCs and NOx from kerosene. Persistent organic pollutant (POPs) emission factors are rarely reported for the sources considered here. Concentrations of the POPs polycyclic aromatics hydrocarbons (PAHs) north of the Niger Delta are among the highest in the world,58 necessitating better understanding of the contributing sources. We have developed the DICE-Africa inventory to address missing, outdated, and misrepresented data in global and regional inventories and assess the effect of these sources on ambient air quality. We obtain NMVOC emissions in Africa that are comparable with estimates from global inventories, but with a very different distribution of contributing compounds. Increases in emissions from 2006 to 2013 are consistent with the RCP scenarios, but weaker than projections from a fuel consumption model. The satellite record shows slow growth in pollution in urban centers in Africa, similar to the trends in this work. We estimate with GEOS-Chem that diffuse and inefficient combustion sources contribute 5 μg m−3 to annual mean surface PM2.5 in populous Nigeria. The contribution of anthropogenic diffuse and inefficient combustion to surface ozone is small compared to that from biomass burning, but this may change as NOx emissions increase with increased use of backup generators and cars. Africa is 20% of the Earth’s land surface, and is projected to be 40% of Earth’s population by 2100.59 There is pressing need for comprehensive country-level activity data and sustained in situ measurements to better constrain diffuse pollution sources in Africa, and assess the impact on global atmospheric composition and local air quality.

Figure 4. Air quality impact of diffuse and inefficient sources of combustion. Maps show annual mean surface PM2.5 and ozone for 2006 obtained as the difference between GEOS-Chem simulations with and without pollution sources calculated in this work (see text for details).

combustion emissions to annual mean surface PM2.5 and ozone, obtained as the difference between simulations with and without DICE-Africa emissions. Contribution to surface concentrations of pollutants is highest in populous areas (Figure 1 and Supporting Information, Figure S1). The largest enhancement in pollutant concentrations is in southern Nigeria. There diffuse and inefficient sources of combustion contribute 5 μg m−3 to annual mean surface PM2.5. Enhancements in the cancer-causing pollutants benzene and formaldehyde, shown in the Supporting Information (Figure S3), exhibit similar spatial patterns to PM2.5. The increase in annual mean surface ozone is small (40 ppbv).1 Biomass burning inventories estimate much larger NOx emissions in 2006 (4.0 Tg NO from FINN and 7.1 Tg NO from GFED3) than DICE-Africa (1.0 Tg NO). Backup generators and cars are a growing source of NOx in Africa (Figure 2) and so increased use of both will lead to very high ozone production efficiency, due to sensitivity of ozone production to the high NMVOC-to-NOx emission ratios typical of inefficient combustion.56 Long-lived NMVOCs and CO contribute 0.29 Dobson Units (DU; 1 DU = 2.69 × 1016 molecules cm−2) to tropospheric ozone averaged across 50°N−

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b02602. Detailed methodology to derive emissions and figures of population distribution, sector-specific emissions, and simulated contribution of diffuse and inefficient combustions sources to annual mean surface formaldehyde and benzene concentrations and tropospheric ozone (PDF) Tables of country-level population, activity data, and emission factors (XLSX)

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1-617-384-7460; e-mail: [email protected]. Notes

The authors declare no competing financial interest. 10743

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and temporal variations, impacts and alleviation. Atmos. Environ. 2015, 118, 184−193. (19) Elvidge, C. D.; Ziskin, D.; Baugh, K. E.; Tuttle, B. T.; Ghosh, T.; Pack, D. W.; Erwin, E. H.; Zhizhin, M. A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data. Energies 2009, 2, 595−622. (20) EIA. US Natural Gas Total Consumption. US Energy Information Administration. https://www.eia.gov/dnav/ng/hist/ n9140us2A.htm, 2016 (accessed May 14, 2016). (21) Marais, E. A.; Jacob, D. J.; Wecht, K.; Lerot, C.; Zhang, L.; Yu, K.; Kurosu, T. P.; Chance, K.; Sauvage, B. Anthropogenic emissions in Nigeria and implications for atmospheric ozone pollution: A view from space. Atmos. Environ. 2014, 99, 32−40. (22) EC-JRC/PBL, European Commission (EC), Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL). Emission Database for Global Atmospheric Research (EDGAR), release version 4.2. http://edgar.jrc.ec.europa.eu, 2011 (accessed September 10−16, 2015). (23) Schultz, M. G.; Oom, D.; Backman, L.; Wittrock, F. REanalysis of the TROpospheric chemical composition over the past 40 years (RETRO)A long-term global modeling study of tropospheric chemistry: Final Report; Jülich/Hamburg, Germany, August, 2007. (24) Lamarque, J. F.; Shindell, D. T.; Josse, B.; Young, P. J.; Cionni, I.; Eyring, V.; Bergmann, D.; Cameron-Smith, P.; Collins, W. J.; Doherty, R.; et al. The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics. Geosci. Model Dev. 2013, 6, 179−206. (25) Bond, T. C.; Streets, D. G.; Yarber, K. F.; Nelson, S. M.; Woo, J. H.; Klimont, Z. A technology-based global inventory of black and organic carbon emissions from combustion. J. Geophys. Res. 2004, 109, 03697 DOI: 10.1029/2003JD003697. (26) Bond, T. C.; Bhardwaj, E.; Dong, R.; Jogani, R.; Jung, S. K.; Roden, C.; Streets, D. G.; Trautmann, N. M., Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850−2000. Global Biogeochem. Cy. 2007, 21, doi:10.1029/ 2006GB002840. (27) Yevich, R.; Logan, J. A., An assessment of biofuel use and burning of agricultural waste in the developing world. Global Biogeochem. Cy. 2003, 17, doi:10.1029/2002GB001952. (28) Keller, C. A.; Long, M. S.; Yantosca, R. M.; Da Silva, A. M.; Pawson, S.; Jacob, D. J. HEMCO v1.0: a versatile, ESMF-compliant component for calculating emissions in atmospheric models. Geosci. Model Dev. 2014, 7, 1409−1417. (29) Akagi, S. K.; Yokelson, R. J.; Wiedinmyer, C.; Alvarado, M. J.; Reid, J. S.; Karl, T.; Crounse, J. D.; Wennberg, P. O. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys. 2011, 11, 4039−4072. (30) Liousse, C.; Penner, J. E.; Chuang, C.; Walton, J. J.; Eddleman, H.; Cachier, H. A global three-dimensional model study of carbonaceous aerosols. J. Geosphys. Res. 1996, 101, 19411−19432. (31) Stockwell, C. E.; et al. Trace gas emissions from combustion of peat, crop residue, domestic biofuels, grasses, and other fuels: configuration and Fourier Transform InfraRed (FTIR) component of the fourth Fire Lab at Missoula Experiment (FLAME-4). Atmos. Chem. Phys. 2014, 14, 9727−9754. (32) Lam, N. L.; et al. Household light makes global heat: High black carbon emissions from kerosene wick lamps. Environ. Sci. Technol. 2012, 46, 13531−13538. (33) UN, United Nations (UN) Data Portal, Energy Statistics Database. United Nations Statistics Division (UNSD). http://data.un. org/Explorer.aspx?d=EDATA 2015 (accessed August 19−30, 2015). (34) HarvestChoice, Agro-ecological Zones of Sub-Saharan Africa. http://harvestchoice.org/node/8853, 2010 (accessed August 6, 2015). (35) EIA, US Energy Information Administration (EIA), International Energy Statistics: Total Electricity Installed Capacity. http:// www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm, 2015 (accessed March 2, 2016).

ACKNOWLEDGMENTS This work was funded by a South African National Research Foundation Fellowship and Schlumberger Faculty for the Future Fellowship to EAM. The National Center for Atmospheric Research is operated by the University Corporation for Atmospheric Research under the sponsorship of the National Science Foundation. CW acknowledges support from the U.S. National Science Foundation Award No. 1211668 and U.S. Environmental Protection Agency STAR Award No. 83542401.

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REFERENCES

(1) Aghedo, A. M.; Schultz, M. G.; Rast, S. The influence of African air pollution on regional and global tropospheric ozone. Atmos. Chem. Phys. 2007, 7, 1193−1212. (2) Liousse, C.; Assamoi, E.; Criqui, P.; Granier, C.; Rosset, R. Explosive growth in African combustion emissions from 2005 to 2030. Environ. Res. Lett. 2014, 9, 035003. (3) IEA. Africa Energy Outlook: A focus on energy prospects in SubSaharan Africa. https://www.iea.org/publications/freepublications/ publication/WEO2014_AfricaEnergyOutlook.pdf, 2014 (accessed August 28, 2016). (4) UN. World Population Prospects: The 2015 Revision. UN Population Division. http://esa.un.org/unpd/wpp/DVD, 2015 (accessed August 19, 2015). (5) WorldBank. Data Portal: Electricity Access. http://data.worldbank. org/indicator/EG.ELC.ACCS.ZS, 2016 (accessed April 8, 2016). (6) Eberhard, A., Foster, V., Briceno-Garmendia, C., Ouedraogo, F., Camos, D., Shkaratan, M. Africa Infrastructure Country Diagnostic (AICD); World Bank: UK, 2008. (7) Girard, P. Charcoal production and use in Africa: what future?; International Cooperation Centre of Agricultural Research for Development (CIRAD-Forêt): Montpellier, France, 2002. (8) Bacon, R., Bhattacharya, S., Kojima, M. Expenditure of Low-Income Households on Energy: Evidence from Africa and Asia; World Bank: Washington, DC, 2010. (9) Tracy, J., Jacobson, A. Lighting Africa: The True Cost of Kerosene in Rural Africa; International Finance Corporation, World Bank: Nairobi, Kenya, 2012. (10) Foster, V., Steinbuks, J. Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity for Firms in Africa; Africa Sustainable Development Front Office, World Bank: UK, April, 2009. (11) Sander, K., Hyseni, B., Haider, W. Wood-Based Biomass Energy Developent for Sub-Saharan Africa: Issues and Approaches; Africa Renewable Energy Access Program (AFREA): Washington, DC, 2011. (12) van Beukering, P., Kahyarara, G., Massey, E., di Prima, S., Hess, S., Makundi, V., van der Leeuw, K. Optimization of the charcoal chain in Tanzania; Poverty Reduction and Environmental Management (PREM): Amsterdam, The Netherlands, May 2007. (13) Baumbach, G.; Vogt, U.; Hein, K. R. G.; Oluwole, A. F.; Ogunsola, O. J.; Olaniyi, H. B.; Akeredolu, F. A. Air-pollution in a large tropical city with a high traffic density - Results of measurements in Lagos, Nigeria. Sci. Total Environ. 1995, 169, 25−31. (14) Assamoi, E. M.; Liousse, C. A new inventory for two-wheel vehicle emissions in West Africa for 2002. Atmos. Environ. 2010, 44, 3985−3996. (15) Kumar, A. Understanding the emerging role of motorcycles in African cities: A political economy perspective; World Bank: Washington, DC, April 2011. (16) SDN. Communities Not Criminals: Illegal Oil Refining in the Niger Delta; Stakeholder Democracy Network (SDN): London, UK, October 16, 2013. (17) EIA. Country Analysis Brief: Nigeria. https://www.eia.gov/ beta/international/analysis_includes/countries_long/Nigeria/nigeria. pdf, 2016 (accessed May 13, 2016). (18) Anejionu, O. C. D.; Whyatt, J. D.; Blackburn, G. A.; Price, C. S. Contributions of gas flaring to a global air pollution hotspot: Spatial 10744

DOI: 10.1021/acs.est.6b02602 Environ. Sci. Technol. 2016, 50, 10739−10745

Article

Environmental Science & Technology (36) UNDP. Standardized Baseline Assessment for Rural Off-Grid Electrification in Sub-Saharan Africa; United Nations Development Programme (UNDP): Addis Ababa, Ethiopia, November, 2013. (37) Elvidge, C. D.; Baugh, K. E.; Tuttle, B. T.; Howard, A. T.; Pack, D. W.; C, M.; Erwin, E. H., A Twelve Year Record of National and Global Gas Flaring Vol.s Estimated Using Satellite Data: Final Report to the World Bank; NOAA: Boulder, CO, May 30, 2007. (38) Bright, E. A.; Coleman, P. R.; King, A. L. LandScan 2006; Oak Ridge National Laboratory: Oak Ridge, TN, 2007. (39) Bright, E. A.; Rose, A. N.; Urban, M. L.. LandScan 2013; Oak Ridge National Laboratory: Oak Ridge, TN, 2014. (40) Mao, J.; et al. Chemistry of hydrogen oxide radicals (HOx) in the Arctic troposphere in spring. Atmos. Chem. Phys. 2010, 10, 5823− 5838. (41) Mao, J.; Jacob, D. J.; Cohen, R. C.; Crounse, J. D.; Wennberg, P. O.; Keller, C. A.; Hudman, R. C.; Barkley, M. P.; Horowitz, L. W.; Paulot, F. Ozone and organic nitrates over the eastern United States: Sensitivity to isoprene chemistry. J. Geosphys. Res. 2013, 118, 11256− 11268. (42) Wiedinmyer, C.; Yokelson, R. J.; Gullett, B. K. Global emissions of trace gases, particulate matter, and hazardous air pollutants from open burning of domestic waste. Environ. Sci. Technol. 2014, 48, 9523−9530. (43) van der Werf, G. R.; et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997−2009). Atmos. Chem. Phys. 2010, 10, 11707−11735. (44) Guenther, A. B.; Jiang, X.; Heald, C. L.; Sakulyanontvittaya, T.; Duhl, T.; Emmons, L. K.; Wang, X. The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 2012, 5, 1471−1492. (45) Hudman, R. C.; Moore, N. E.; Mebust, A. K.; Martin, R. V.; Russell, A. R.; Valin, L. C.; Cohen, R. C. Steps towards a mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints. Atmos. Chem. Phys. 2012, 12, 7779−7795. (46) Wiedinmyer, C.; Akagi, S. K.; Yokelson, R. J.; Emmons, L. K.; Al-Saadi, J. A.; Orlando, J. J.; Soja, A. J. The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geosci. Model Dev. 2011, 4, 625−641. (47) EIA, US Energy Information Administration (EIA), Today in Energy: Density and sulfur content of selected crude oils. http://www. eia.gov/todayinenergy/detail.cfm?id=18571, 2013 (accessed May 13, 2016). (48) Thomson, A. M.; et al. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 2011, 109, 77−94. (49) Riahi, K.; Rao, S.; Krey, V.; Cho, C. H.; Chirkov, V.; Fischer, G.; Kindermann, G.; Nakicenovic, N.; Rafaj, P. RCP 8.5-A scenario of comparatively high greenhouse gas emissions. Clim. Change 2011, 109, 33−57. (50) Henze, D. K.; Seinfeld, J. H.; Ng, N. L.; Kroll, J. H.; Fu, T. M.; Jacob, D. J.; Heald, C. L. Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high- vs. low-yield pathways. Atmos. Chem. Phys. 2008, 8, 2405−2420. (51) Ryerson, T. B.; et al. Observations of ozone formation in power plant plumes and implications for ozone control strategies. Science 2001, 292, 719−723. (52) Kleinman, L. I.; Daum, P. H.; Lee, Y. N.; Nunnermacker, L. J.; Springston, S. R.; Weinstein-Lloyd, J.; Rudolph, J. Ozone production efficiency in an urban area. J. Geosphys. Res. 2002, 107, 23-1. (53) De Smedt, I.; Stavrakou, T.; Muller, J. F.; van der A, R. J.; Van Roozendael, M. Trend detection in satellite observations of formaldehyde tropospheric columns. Geophys. Res. Lett. 2010, 37, 044245. (54) Hilboll, A.; Richter, A.; Burrows, J. P. Long-term changes of tropospheric NO2 over megacities derived from multiple satellite instruments. Atmos. Chem. Phys. 2013, 13, 4145−4169. (55) Schneider, P.; Lahoz, W. A.; van der A, R. Recent satellite-based trends of tropospheric nitrogen dioxide over large urban agglomerations worldwide. Atmos. Chem. Phys. 2015, 15, 1205−1220.

(56) Sillman, S. The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ. 1999, 33, 1821−1845. (57) Mickley, L. J.; Murti, P. P.; Jacob, D. J.; Logan, J. A.; Koch, D. M.; Rind, D. Radiative forcing from tropospheric ozone calculated with a unified chemistry-climate model. J. Geosphys. Res. 1999, 104, 30153−30172. (58) Ana, G. R. E. E.; Sridhar, M. K. C.; Emerole, G. O. Polycyclic aromatic hydrocarbon burden in ambient air in selected Niger Delta communities in Nigeria. J. Air Waste Manage. Assoc. 2012, 62, 18−25. (59) UN. World Population Prospects: The 2015 Revision; United Nations Department of Economic and Social Affairs: New York, NY, 2015.

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